
'''simple docstring'''

import argparse
import os

from accelerate.test_utils import execute_subprocess_async







def __A    (       lowerCAmelCase_=None			):

     if subparsers is not None:
          _UpperCAmelCase   :    Optional[Any]			    =       subparsers.add_parser("""test"""			)
     else:
          _UpperCAmelCase   :    List[str]			    =       argparse.ArgumentParser("""Accelerate test command"""			)

     parser.add_argument(
         """--config_file"""					,			default=_A					,			help=(
             """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
             """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
             """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
             """with 'huggingface'."""
         )					,			)

     if subparsers is not None:
          parser.set_defaults(func=_A			)
     return parser







def __A    (       lowerCAmelCase_			):
     _UpperCAmelCase   :    Optional[int]			    =       os.path.sep.join(__file__.split(os.path.sep			)[:-2] + ["""test_utils""", """scripts""", """test_script.py"""]			)

     if args.config_file is None:
          _UpperCAmelCase   :    List[str]			    =       script_name
     else:
          _UpperCAmelCase   :    Optional[Any]			    =       f"--config_file={args.config_file} {script_name}"

     _UpperCAmelCase   :    Optional[int]			    =       ["""accelerate-launch"""] + test_args.split()
     _UpperCAmelCase   :    int			    =       execute_subprocess_async(_A					,			env=os.environ.copy()			)
     if result.returncode == 0:
          print("""Test is a success! You are ready for your distributed training!"""			)







def __A    (       ):
     _UpperCAmelCase   :    Any			    =       test_command_parser()
     _UpperCAmelCase   :    str			    =       parser.parse_args()
     test_command(_A			)


if __name__ == "__main__":
   main()




"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)